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Figure 1-4 Problem reduction search building the solution, an AND tree Page 13 subgoals. Figure 1-3 gives the algorithm and the general form of the programs. The related algorithm is one which constructs the solution AND tree (not a path, as in state-space search) as a product of its search. Figure 1-4 gives the general form of the programs. Both sentence parsers and expert systems can be built out of variations of the programs in Figure 1-4. e. self-contained, named segments of code which return a result given a number of arguments.

Then, for each successor we set up a new list of all its successors. Then, for each successor in each one of these successor lists we set up a list of all its successors, and so on. e. nested lists), the first part of which looks like this: [old_steine [the_level [preston_circus] [the_parallels]] [the_parallels [preston_circus] [the_level] [clocktower]] [palace_pier [west_pier]]] These lists form a tree structure, although as printed above, the tree is upside-down and leaning to the left. The word old_steine forms the root of the tree; this splits into three main branches corresponding to the_level, the_parallels and palace_pier; then the first of these branches itself splits into two sub-branches corresponding to preston_circus and the_parallels etc.

Imagine that we select some arbitrary location in the hypothetical version of Brighton (old_steine say) and construct a list of all its successors. Then, for each successor we set up a new list of all its successors. Then, for each successor in each one of these successor lists we set up a list of all its successors, and so on. e. nested lists), the first part of which looks like this: [old_steine [the_level [preston_circus] [the_parallels]] [the_parallels [preston_circus] [the_level] [clocktower]] [palace_pier [west_pier]]] These lists form a tree structure, although as printed above, the tree is upside-down and leaning to the left.

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